电子测量与仪器学报

罗其俊,相承志,张红颖.基于骨架点云配准的飞机泊位实时位姿估计方法[J].电子测量与仪器学报,2025,39(1):156-164

基于骨架点云配准的飞机泊位实时位姿估计方法

Fast pose estimation algorithm for berth aircraft basedon skeleton point cloud registration

DOI：

英文关键词:point cloud registration aircraft berth pose estimation skeleton point cloud overlapping area

基金项目:中央高校基本科研业务费项目(3122018D002)资助

作者	单位
罗其俊	中国民航大学电子信息与自动化学院天津300300
相承志	中国民航大学电子信息与自动化学院天津300300
张红颖	中国民航大学电子信息与自动化学院天津300300

Author	Institution
Luo Qijun	School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Xiang Chengzhi	School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Zhang Hongying	School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

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中文摘要:

利用激光雷达扫描的三维点云，准确快速地计算飞机位姿，是实现飞机自动泊位引导的关键。为此，提出一种基于骨架点云精确配准的快速目标位姿估计算法。首先，在飞机表面点云中，选取激光雷达视角下的机翼、发动机和机头等主要机体结构，构建飞机骨架点云，避免点云复杂结构导致的错误配准，并有效降低计算量。在飞机泊位时，建立基于飞机轴向的点云包围盒，以获取飞机初始位姿，并将其作为配准的约束条件，避免局部最优。然后，采用快速点特征描述的随机抽样一致性粗配准算法，校正飞机位姿，并设计基于双向KD-Tree的点面精配准算法，提高飞机位姿估计的精度。最后，通过泊位全过程飞机位姿估计的仿真实验，验证了算法性能。较Super-4PCS、MSKM-NDT和AA-ICP等典型算法，配准误差降低32.5%，处理速度提升34%。位姿估计的最大角度误差为2.0°，最大距离误差为0.125 m，单帧处理速度为0.37 s。实际的飞机位姿估计实验，也验证了算法的有效性。

英文摘要:

The accurate and rapid calculation of aircraft pose using three-dimensional point clouds scanned by LiDAR is the key to achieving automatic parking guidance for aircraft. Therefore, a fast target pose estimation algorithm based on precise registration of skeleton point clouds is proposed. In the point cloud on the aircraft surface, the main body structures such as wings, engines, and nose are selected from the perspective of LiDAR to construct a simplified aircraft skeleton point cloud, avoiding erroneous registration of other complex structures and effectively reducing computational complexity. When parking the aircraft, establish a point cloud bounding box based on the aircraft axis to obtain the initial pose of the aircraft and use it as a constraint for registration. Then, a random sampling consistent coarse registration algorithm based on fast point feature description is used to correct the aircraft pose, and a point surface fine registration algorithm based on bidirectional KD-Tree is designed to improve the accuracy of aircraft pose estimation. Finally, the performance of the algorithm was validated through simulation experiments on aircraft pose estimation throughout the entire parking process. Compared with typical algorithms such as Super-4PCS, MSKM-NDT, and AA-ICP, this paper’s algorithm reduces registration error by 32.5% and improves processing speed by 34%. The maximum angle error for pose estimation is 2.0 degrees, the maximum distance error is 0.125 meters, and the single frame processing speed is 0.37 seconds. The actual aircraft pose estimation experiment also verified the effectiveness of the algorithm.

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